The Potential of LLMs in Automating Software Testing: From Generation to Reporting
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909445844369408 |
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| author | Sherifi, Betim Slhoub, Khaled Nembhard, Fitzroy |
| author_facet | Sherifi, Betim Slhoub, Khaled Nembhard, Fitzroy |
| contents | Having a high quality software is essential in software engineering, which requires robust validation and verification processes during testing activities. Manual testing, while effective, can be time consuming and costly, leading to an increased demand for automated methods. Recent advancements in Large Language Models (LLMs) have significantly influenced software engineering, particularly in areas like requirements analysis, test automation, and debugging. This paper explores an agent-oriented approach to automated software testing, using LLMs to reduce human intervention and enhance testing efficiency. The proposed framework integrates LLMs to generate unit tests, visualize call graphs, and automate test execution and reporting. Evaluations across multiple applications in Python and Java demonstrate the system's high test coverage and efficient operation. This research underscores the potential of LLM-powered agents to streamline software testing workflows while addressing challenges in scalability and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Potential of LLMs in Automating Software Testing: From Generation to Reporting Sherifi, Betim Slhoub, Khaled Nembhard, Fitzroy Software Engineering Artificial Intelligence Having a high quality software is essential in software engineering, which requires robust validation and verification processes during testing activities. Manual testing, while effective, can be time consuming and costly, leading to an increased demand for automated methods. Recent advancements in Large Language Models (LLMs) have significantly influenced software engineering, particularly in areas like requirements analysis, test automation, and debugging. This paper explores an agent-oriented approach to automated software testing, using LLMs to reduce human intervention and enhance testing efficiency. The proposed framework integrates LLMs to generate unit tests, visualize call graphs, and automate test execution and reporting. Evaluations across multiple applications in Python and Java demonstrate the system's high test coverage and efficient operation. This research underscores the potential of LLM-powered agents to streamline software testing workflows while addressing challenges in scalability and accuracy. |
| title | The Potential of LLMs in Automating Software Testing: From Generation to Reporting |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2501.00217 |